A lot of software teams already have AI somewhere in the development process. The bigger change starts when the AI is no longer waiting for a developer to move the work along.
A ticket can become a plan, the plan becomes code, tests fail and get investigated. Eventually a pull request comes back for review without somebody manually pushing the task through every intermediate step.
Developers used to be present for most of those transitions.
They noticed when a requirement was vague, when a change had drifted beyond the ticket or when the obvious implementation conflicted with what another team was doing.
An agentic SDLC starts to emerge when that extra execution capacity appears across planning, development, testing, and deployment instead of staying inside the coding tool.
What is an agentic SDLC?
An agentic software development lifecycle (SDLC) is one where AI agents can execute parts of software work toward outcomes set by people. A useful distinction from ordinary AI-assisted development is who keeps the task moving.
One developer using AI might ask for an implementation, inspect it, run the tests, bring the failure back to the model and decide what happens next. AI appears throughout the workflow but the developer is still connecting the stages.
Planning, implementation, review and testing can be handled by different agents or tools. What makes the lifecycle agentic is that the work can keep moving between those stages without a developer manually restarting the process each time.
In our experience, autonomy tends to be uneven. Implementation might run unattended despite plan approval, review or deployment still belonging to people, and that boundary can be different for every workflow.
Humans can still own decisions around the problem being solved, architecture, UX, functional requirements, compliance or governance. Yet agents handle much more of the implementation between them.
How do AI agents change the software development lifecycle?
The lifecycle doesn’t suddenly become autonomous end to end. What changes first is the handoff between stages. Work that used to stop with a developer can keep moving, which means gaps that were once caught informally have to be handled somewhere in the workflow.
Planning is one of the first places that becomes obvious.
Planning: agents expose gaps in intent
For a ticket that says: “add team sharing” some ordinary engineering work is hiding behind that sentence. It could be an API, some persistence, some UI.
The team needs answers to some product questions too.
Who can invite somebody?
What happens when the owner leaves?
Does a new member get access to resources created before they joined?
Who pays?
A developer eventually reaches those questions and asks somebody but an agent may simply choose an answer and continue.Stronger models make that harder to spot because the result may look perfectly reasonable. You don’t necessarily get broken code.
You’ll probably get a clean, well-tested implementation of behavior that nobody actually decided on.
The agent can propose a plan first, giving people a chance to correct the product or architecture decisions that actually matter before implementation gets too far.
Same problem shows up with changes that sound more technical.
“Add enterprise SSO” still leaves questions about what happens to existing customers, which service owns authentication and whether the dependency the agent wants to use is actually approved.
Development: the execution environment becomes part of the workflow
A background agent still needs the repository, build dependencies, and whatever access the task actually requires.
Sounds obvious.
Until you compare it with a developer’s laptop where much of this infrastructure already exists and has slowly faded into the background without anyone realizing. A simple test repair might need little beyond the code and test runner. Another task could need staging, an internal system or credentials that should only exist for that run.
If planning produced a constraint such as “don’t change the public API,” that decision needs to survive into implementation and review. Otherwise every stage can be individually capable while the handoff between them loses part of the original intent.
Now the environment has to match the work.
A routine maintenance task and a production-sensitive migration may use the same coding agent while needing very different access.
And once those environments need to be shared across teams, you’re moving into Software Factory territory.
Testing and review absorb more pressure
Existing tests, CI, and security checks are still useful and play an important role. In some ways they matter more once agents can produce more work without somebody watching each intermediate step.
Review also doesn’t have to move directly from the coding agent to a human. Automated or agent-based review can happen first with humans coming in where judgment is still needed.
Capacity ends up being the real issue. If implementation gets cheaper and faster, review becomes the bottleneck instead.
Producing more code doesn’t produce more senior reviewers, architecture judgment or product knowledge.
A green test suite can tell you that the implementation behaves consistently with the tests. It can’t tell you whether “team sharing” reflects the behavior somebody intended if that decision was never made.
There’s another wrinkle when the agent writes the tests too. The implementation and the verification can share the same misunderstanding. If the agent decided that a new team member should automatically see historical resources, it can write both the feature and a test that confirms exactly that behavior.
Independent checks matter more when the same system is producing both the answer and some of the evidence that the answer is correct.
That independence doesn’t necessarily require another AI agent. It might be an existing test, a policy check, a security rule or a human review point that wasn’t created from the same assumption as the implementation.
Architecture has the same problem. Code can compile, pass every existing check and still reinforce a pattern the enterprise is trying to retire. Good review was already scarce in plenty of engineering teams. Agentic development can make that much easier to see.
Deployment can keep a different level of autonomy
An enterprise may be comfortable letting an agent implement and test a routine change while still requiring somebody to approve production deployment.
Another workflow might automate more because the scope is narrow, the checks are strong and rollback is well understood.
A change doesn’t have to jump from an agent-generated pull request straight to full production either. It could move through a staged rollout first, with error rates or other production signals determining whether the release continues or gets rolled back.
Production also gives you information that didn’t exist during implementation. A change can pass review and every automated check, then increase errors or trigger a rollback once it reaches the real system.
If the enterprise can connect that outcome back to the workflow that produced the change, that becomes useful evidence when deciding what the agent should be allowed to do next time.
Governance across an agentic SDLC
Once an agent can act across several stages, controls have to appear wherever the work needs them.
The agent needs an identity and access appropriate to the task. Something allowed during development may not be allowed during deployment. Sensitive source code may be restricted to particular model providers or environments while another repository has fewer constraints.
Human approval gets more interesting too.
The gate can sit around a particular decision rather than the whole task. An agent might be free to edit code and run tests, then stop before a schema migration, authentication change or action involving a sensitive internal system.
Approving a product decision, an architecture exception or a security-risk acceptance is very different from repeatedly approving a plan that engineers rarely change.
The interventions themselves tell you something. If engineers keep stepping in, maybe the agent had poor context. Maybe it was missing access. Maybe the automated checks weren’t strong enough. Or maybe the task still contains a decision that genuinely belongs with a person.
Those are different engineering problems.
The detailed machinery for permissions, evidence and workflow controls sits below the lifecycle itself, but the practical point is that governance can’t be treated as one final approval step once the agent is acting much earlier in the process.
How do you introduce agentic workflows into the SDLC?
A useful starting point is work the team already understands. Vulnerability remediation, routine bug fixes, migrations and maintenance changes may all end in code.
However they behave very differently as agent workflows. Starting with something familiar makes failures much easier to interpret.
You quickly start seeing different kinds of intervention. Sometimes the agent stops because a decision was never documented, the plan goes through with almost no changes and the real problems appear in code review, or the implementation is fine and the environment simply doesn’t have access to something it needs.
Those cases shouldn’t all be filed under “the agent needed a human.”
One may point to missing context, another to the execution environment, and another may simply be a judgment call that should continue going to a person.
The task doesn’t have to be tiny either.
Familiarity can matter more than size because the team needs enough understanding of the workflow to recognize when the agent has gone somewhere strange.
Over time, those outcomes become part of the lifecycle itself. Planning can learn from review corrections, implementation can inherit better constraints, and deployment failures can change how much freedom the same workflow gets next time. The lifecycle becomes less linear once evidence from later stages starts changing how earlier stages behave.
How does an agentic SDLC fit with DevOps, platform engineering and CI/CD?
An agentic SDLC still relies on the delivery system enterprises already have.
CI/CD continues to build, test and deploy software. Platform engineering still provides environments, internal services and shared infrastructure. DevOps practices still shape how changes reach production and what happens when something goes wrong there.
What changes is how much work an agent can perform while moving through those systems. A developer doesn’t have to manually connect every step between a ticket, repository, test result and next action anymore. You end up putting more pressure on the underlying engineering environment to behave consistently.
Humans are surprisingly good at working around the opposite. They know an internal guide is outdated, remember that one service has a strange setup step, and realize that the documented API doesn’t quite behave the way the page says it does.
Background agents have a much harder time doing that reliably. A broken setup guide that an engineer has ignored for six months suddenly matters when every background run follows it literally.
As more of that work moves into the background, the surrounding context, access and controls have to become part of the workflow too.
If your team is starting to run agentic workflows this way, book a demo to see how Orq.ai can support the infrastructure around them
Frequently asked questions
Is an agentic SDLC the same as autonomous software development?
No. Agents can execute substantial parts of a workflow while people still make decisions during planning, review, deployment or other stages. Different workflows can operate with different levels of autonomy.
Does an agentic SDLC replace CI/CD or DevOps?
No. Agent workflows generally use the build, test, deployment and platform systems enterprises already have. The change is that agents can carry more of the work through those systems without a developer directing every intermediate step.
Where should humans remain in the loop?
Where people are still adding judgment that the workflow can’t reliably get from its context, policies or automated checks. That boundary can move as teams learn from repeated runs and improve the workflow.
